Natural Language Processing (NLP)

Deep Learning and Financial Stability ArXiv ID: ssrn-3723132 “View on arXiv” Authors: Unknown Abstract The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation. flowchart TD A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"] B --> C["Computational Processes"] C --> D["Key Findings & Outcomes"] B --> B1["Multi-Asset Data"] B --> B2["NLP on Financial Text"] B --> B3["Alternative Data Sources"] C --> C1["Deep Learning Models"] C --> C2["Financial Stability Metrics"] C --> C3["Risk Assessment Algorithms"] D --> D1["Enhanced Risk Prediction"] D --> D2["Systemic Stability Insights"] D --> D3["Fintech Innovation Pathways"] style A fill:#e1f5fe style D fill:#e8f5e8

Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’ ArXiv ID: ssrn-3661469 “View on arXiv” Authors: Unknown Abstract Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma Keywords: Artificial Intelligence (AI), Neural Networks, Natural Language Processing (NLP), Deep Learning, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual literature review discussing AI applications in banking with no mathematical formulas or statistical models, and its empirical backing is limited to citing other studies without original data analysis or backtesting. flowchart TD A["Research Question:<br>How is AI changing modern banks?"] --> B["Methodology:<br>Review of Neural Networks, NLP, Deep Learning"] B --> C["Inputs:<br>Banking data & AI Equities"] C --> D["Computational Process:<br>AI Simulation of Human Intelligence"] D --> E["Key Findings:<br>Banking 4.0 Transformation"]